Nevertheless, both two-step and one-step methods concentrate on the person-centric regions, without perceiving the surrounding environment. In other words, the holistic and rich context is not fully exploited.
However, mining such context is proved to be very effective in other related tasks, such as object detection [1]}, [2]}, [3]} and group re-ID [4]}, [5]}. In small object detection [1]}, [2]}, scene context help find the tiny objects which are usually neglected by common object detection methods. Group re-ID methods [8]}, [9]}, [10]} usually enhance the re-ID embeddings of the target person by incorporating the features of neighboring persons.
Motivated by the above facts and owing to the setting of person search (i.e., a holistic image including multiple persons is provided), several attempts [11]}, [12]}, [13]}, [14]} have recently been made to learn the underlining context information beyond the detected person regions. They typically exploit two types of context information, i.e., scene and group. For instance, scene context is used in [11]}, [12]} to help identify the target via measuring the correspondence between the detected box of the query and the scene in the gallery. Group context is often mined in a pair-wise fashion, e.g., siamese networks are developed in [13]}, [14]} to find the potential co-travelers; [19]}, [20]}, [13]} attempt to achieve the globally optimized matching based on the input pairs.
